Nlogistic-sigmoid function (NLSIG) is a modern logistic-sigmoid function definition for modelling growth (or decay) processes. It features two logistic metrics (YIR and XIR) for monitoring growth from a two-dimensional (x-y axis) perspective.

**Bi3D** is a stereo depth estimation framework that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth *D*, as existing stereo methods do, it classifies them as being closer or farther than *D*. It takes the stereo pair and a disparity $d\_{i}$ and produces a confidence map, which can be thresholded to yield the binary segmentation. To estimate depth on $N + 1$ quantization levels we run this network $N$ times and maximize the probability in Equation 8 (see paper). To estimate continuous depth, whether full or selective, we run the [SegNet](https://paperswithcode.com/method/segnet) block of Bi3DNet for each disparity level and work directly on the confidence volume.

Bi3D

Bi3D: Stereo Depth Estimation via Binary Classifications

NLSIG

The **Cross-Attention** module is an attention module used in [CrossViT](https://paperswithcode.com/method/crossvit) for fusion of multi-scale features. The CLS token of the large branch (circle) serves as a query token to interact with the patch tokens from the small branch through attention. $f\left(·\right)$ and $g\left(·\right)$ are projections to align dimensions. The small branch follows the same procedure but swaps CLS and patch tokens from another branch.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com